Dimensionality reduction beyond neural subspaces with slice tensor component analysis.
Nat Neurosci
; 27(6): 1199-1210, 2024 Jun.
Article
in En
| MEDLINE
| ID: mdl-38710876
ABSTRACT
Recent work has argued that large-scale neural recordings are often well described by patterns of coactivation across neurons. Yet the view that neural variability is constrained to a fixed, low-dimensional subspace may overlook higher-dimensional structure, including stereotyped neural sequences or slowly evolving latent spaces. Here we argue that task-relevant variability in neural data can also cofluctuate over trials or time, defining distinct 'covariability classes' that may co-occur within the same dataset. To demix these covariability classes, we develop sliceTCA (slice tensor component analysis), a new unsupervised dimensionality reduction method for neural data tensors. In three example datasets, including motor cortical activity during a classic reaching task in primates and recent multiregion recordings in mice, we show that sliceTCA can capture more task-relevant structure in neural data using fewer components than traditional methods. Overall, our theoretical framework extends the classic view of low-dimensional population activity by incorporating additional classes of latent variables capturing higher-dimensional structure.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Neurons
Limits:
Animals
Language:
En
Journal:
Nat Neurosci
Journal subject:
NEUROLOGIA
Year:
2024
Document type:
Article
Affiliation country:
France